Coordinate measuring machines (CMMs) are the gold standard for accurately measuring a wide variety of different types of work pieces/objects. For example, CMMs can measure critical dimensions of aircraft engine components, surgical tools, and machine parts. Precise and accurate measurements help ensure that their underlying systems, such as an aircraft in the case of aircraft components, operate as specified.
Some objects are measured to a fine precision, such as on the micron level. The accuracy of a CMM may depend, in part, on the calibration of the CMM and the accuracy of the measuring device (e.g., optical probe) used for the measurement.
A CMM may use one or more types of sensor, such as tactile sensors, touchless sensors, photographic sensors (e.g., video sensors), to measure a workpiece. Calibrating a CMM may involve causing the CMM to measure a calibration artifact of known dimensions, and take remedial steps (e.g., adjust the CMM and/or determine mathematical data for use in correcting measurement data) to mitigate differences between the measurements and the known dimensions of the artifact.
The ISO 10360-9 standard establishes specific procedures for verifying the performance of a CMM that uses multiple probing systems in contacting and non-contacting mode. The standard describes analysis of the quality of the associativity of multiple sensors (e.g., tactile and video sensors) along with their different operating conditions (e.g., the orientation of the sensor as defined by the articulation of a wrist) to assess whether different sensors at different wrist orientations can measure the same artifact and report data on the size, form, and location of that artifact that correlates within some tolerance zone.
For tactile sensors the artifact of choice has traditionally been a calibrated sphere.
For optical sensors, including video sensors, the choice of a sphere as the test artifact would present a unique set of challenges, including the challenge of illumination. The video sensor, for example, operates by detecting edges defined by some contrast in the greyscale analysis of the pixels seen by the sensor's field of view (FOV). In essence, such a sensor probes points by “looking” at the part and choosing the point where the part shows some contrast between black and white.